Data

Tools

Indexes

Applications

Experiments

Open Source Science

listening-beyond-the-labels-v1

A scalable and non-invasive speech-based machine learning model for early Alzheimer's detection using mel-spectrograms and lightweight semi-supervised CNN with no transcription or neuroimaging needed.

https://github.com/arya-gaj/listening-beyond-the-labels-v1

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (6.5%) to scientific vocabulary

Keywords

colab google keras librosa matplotlib numpy os pydub python scikit-learn shutil soundfile tensorflow threadpoolexecutor tqdm
Last synced: 4 months ago · JSON representation ·

Repository

A scalable and non-invasive speech-based machine learning model for early Alzheimer's detection using mel-spectrograms and lightweight semi-supervised CNN with no transcription or neuroimaging needed.

Basic Info
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
colab google keras librosa matplotlib numpy os pydub python scikit-learn shutil soundfile tensorflow threadpoolexecutor tqdm
Created 7 months ago · Last pushed 6 months ago
Metadata Files
Readme Contributing License Citation

README.md

Listening Beyond The Labels

Abstract

Alzheimer's Disease (AD), a progressive neurodegenerative condition of cognitive decline, presents formidable challenges to patients, caregivers, and healthcare systems. Early identification is essential for successful intervention, but traditional diagnosis requires expensive neuroimaging and lengthy clinical assessments, which compromise access. This study proposes a semi-supervised machine learning strategy for AD diagnosis based on acoustic features from brief speech samples. The approach takes advantage of mel-spectrogram features to extract vocal patterns without manual transcription or linguistic preprocessing. Informative sound patterns are detected using a convolutional neural network (CNN) that gradually adds unlabeled speech data during training through pseudo-labeling. By emphasizing scalable, non-invasive methods that rely solely on unprocessed vocal inputs, this work presents a practical solution for large-scale cognitive screening in resource-limited settings.

Owner

  • Name: Aryaman Gajrani
  • Login: arya-gaj
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Gajrani"
  given-names: "Aryaman"
  orcid: "https://orcid.org/0009-0009-7141-8707"
title: "listening-beyond-the-labels"
version: 1.0.0
doi: 10.5281/zenodo.1234
date-released: 2025-07-01
url: "https://github.com/arya-gaj/listening-beyond-the-labels"

GitHub Events

Total
  • Push event: 58
Last Year
  • Push event: 58